Topic: Metal Additive Manufacturing: Alloy Design, Process Physics, Microstructure, and Mechanical Properties

This Special Issue focuses on the scientific foundations that enable metal additive manufacturing (AM) by linking alloy design, process physics, and mechanical properties. We invite contributions across laser powder bed fusion (PBF-LB), directed energy deposition (DED), electron beam powder bed fusion, wire arc AM (WAAM), friction stir AM (FSAM), and emerging metal AM techniques that elucidate how melt pool dynamics, heat flow, solidification pathways, and defect formation (keyhole, lack of fusion, spatter, hot cracking) govern microstructure and performance. Topics of interest include, but are not limited to: AM-tailored alloy chemistries; CALPHAD (CALculation of PHAse Diagrams)-guided and data-driven design; in situ/operando diagnostics (e.g., synchrotron methods, pyrometry, acoustic/optical sensing) and digital twins; mesoscale (phase-field) and crystal-plasticity finite-element (CPFE) modeling of texture evolution and residual stress; and post-processing (e.g., HIP, heat treatment) for microstructure/property control.

We especially welcome studies that rigorously link microstructural features to mechanical behavior—including tension/compression, mechanical anisotropy, fatigue, fracture, and creep—tied to the heterogeneous microstructures characteristic of additively manufactured metals. This Issue also highlights the integration of machine learning (ML) and artificial intelligence (AI) across the AM workflow, including inverse alloy design, active learning/Bayesian optimization, and reinforcement learning for process optimization, as well as physics-informed and uncertainty-aware surrogate models for predicting process–structure–property relationships.

Article types include Original Research, Rapid Communications, Reviews, and Perspectives/Outlooks. By unifying alloy design with process-aware models, ML/AI-enabled workflows, and property validation, this Issue aims to establish transferable principles for predictive, certifiable metal AM technologies.

Metal additive manufacturing, alloy design, process–structure–property relationships, microstructure evolution, machine learning and artificial intelligence, mechanical Behavior